A three-dimensional smartphone positioning method using a spinning magnet marker
Bibliographic record
Abstract
We propose a method of detecting the precise three-dimensional position of a smartphone using a Spinning Magnet Marker (SMM). An SMM is a device that generates a dynamic magnetic field by spinning a strong magnet with a motor. In the proposed method, the magnetic sensor of a smartphone detects the magnetic field generated by an SMM, and the three-imensional position of the smartphone is estimated with an accuracy of better than several tens of centimeters based on the magnetic field and the motor angle of the SMM. It is expected that such precise three-dimensional positioning will enable not only better navigation of users to their destinations but also a better understanding of human behavior. First, we construct theoretical equations relating the magnetic field generated by the SMM to the three-dimensional position of the smartphone in three-dimensional polar coordinates. Second, we evaluate the estimation accuracy of the proposed method with the distance between the SMM and the smartphone fixed at 1.0 m. The azimuth is estimated with a mean error of within 11 degrees, and the elevation is estimated with a mean error of within 10 degrees. The distance is estimated with a mean error of within 19 cm at distances of up to 3.0 m.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".